Practical Fundamentals for Master Data Management
|
|
- Deirdre Barnett
- 8 years ago
- Views:
Transcription
1 Practical Fundamentals for Master Data Management How to build an effective master data capability as the cornerstone of an enterprise information management program WHITE PAPER
2 SAS White Paper Table of Contents Introduction Considering Master Data Management (MDM) Benefits They Sound Good, But Master Data and Real Business Value Assessing Organizational Readiness for Enterprise Initiatives... 4 Horizontal Coordination Coordinating Systemic Change Consistency in Meaning Standardized Business Rules... 5 Practical Master Data Management Technical Data Improvement Reconsidering MDM... 7 Purpose-Driven MDM... 7 Start with the Fundamentals... 8 Standardize Semantics... 8 Consolidate Enterprise Metadata... 8 Improve Data Quality Balance Business and Technical Benefits Establishing the Fundamentals Ready? Set Go! About the Author
3 Practical Fundamentals for Master Data Management Introduction Enterprisewide programs are often designed to improve business processes and business decisions through enhanced information sharing based on a more reliable foundation of data. For this purpose, these initiatives promise significant organizational benefits along a number of dimensions of value, but, as with any business project, they can also carry significant risks and complexity. Organizations that have successfully deployed programs that span the enterprise are prime candidates for a successful enterprise master data management (MDM) initiative; those lacking this experience are at greater risk for MDM failure. However, since the early tangible value of an MDM strategy comes from standardized semantics, consolidated metadata, and most critically, data quality improvement, focusing on these fundamental aspects of MDM reduces the risks while providing practical utility to the data user community. In this paper, we look at the levels of maturity and capability necessary for deploying enterprisewide initiatives, such as comprehensive MDM, and examine why these initiatives stall. In turn, we will consider the types of value derived from MDM and which tangible benefits you can achieve early in the process. Since many of these early benefits concentrate on simplifying and standardizing semantics, managing metadata and improving data quality, this paper suggests that starting with tasks that address those fundamental needs will add value and prepare your organization to take the steps needed to incrementally build the master data capability. In conclusion, we provide some concrete actions that you can take to position those fundamentals as the first step in growing a long-term, business-oriented, enterprise information strategy. Since the early tangible value of an MDM strategy comes from standardized semantics, consolidated metadata, and most critically, data quality improvement, focusing on these fundamental aspects of MDM reduces the risks while providing practical utility to the data user community. 1
4 SAS White Paper Considering Master Data Management (MDM) Benefits New technologies such as master data management are often positioned as silver bullets when it comes to addressing long-standing systemic challenges. Every corporate technology of the preceding decade data warehouses, enterprise resource planning (ERP), customer relationship management (CRM) arrived with similar levels of fanfare to solve the problem of managing data across the enterprise. It should not be surprising that the numerous benefits suggested by the hype surrounding MDM might lead to inflated expectations. Yet when the dust has settled, the tools are installed, and the data integration is done, organizations often find that they are still stalled in progressing toward that elusive 360-degree view of the customer, product or other data element. Perhaps too much faith is placed in the conventional wisdom surrounding the value of MDM. When reviewing the different white papers, vendor briefs, analyst reports or the many articles on the topic, it is valuable to consider the business drivers for and purported benefits of implementing MDM. In general, the presumptive benefits achieved using MDM techniques can be categorized into three classes: Vague statements with no measurable characteristics or performance metrics (such as becoming a smarter organization ). Valuable business benefits for which MDM is necessary but not sufficient (such as reducing merger and acquisition costs ). Data improvement benefits derived from better data quality and data governance practices. The value of any proposed technology must be coupled with hard metrics that can be used to baseline the current state and show measured improvement. They Sound Good, But Senior business executives are focused on the organization s strategic objectives and how performance metrics demonstrate business value. In that light, the value of any proposed technology must be coupled with hard metrics that can be used to baseline the current state and show measured improvement. In the examples above, statements like enabling people to make decisions more quickly or making your organization smarter both sound desirable. However, neither has discrete measures of quantification, and you might ask whether either has any real business meaning at all. After all, it is unclear whether concepts like the speed of decision making or organizational smartness contribute to achieving an organization s strategic objectives. Other examples are equally devoid of substance, such as reducing the count of vendor records or breaking down organizational silos. These are more likely to be outcomes of more direct business process improvements focused on revenue generation, reducing costs or decreasing operational risk, but do not add value in and of themselves. 2
5 Practical Fundamentals for Master Data Management Essentially, while we are presented with a large number of potentially desirable outcomes, not all of these are directly associated with creating value. And without a way to quantify the business value of a purported benefit of a technology, you would find it difficult to use that benefit to justify moving forward with that technology. Master Data and Real Business Value It is the synergy of high-quality data and good business processes that you can use to exploit information. Using the existence of a quantifiable metric to measure business value as a filter, we can quickly strike the vaguely defined benefits off our list, allowing us to focus on real business value drivers such as improving customer retention, increasing revenue, simplifying supplier onboarding, reducing risk or lowering costs. These measurable business value drivers are definitely affected by the use of a high-quality, unified view of critical data domains such as customer and product. In these cases, MDM contributes to achieving the business goal, but it alone does not guarantee the business benefit. Increased visibility into customer data helps staff members evaluate the various customer interaction touch points and analyze process effectiveness. But it is the synergy of high-quality data and good business processes that you can use to exploit information. The combination of visibility into customer information, behavior profiling, predictive analytics, and most importantly, the business processes for operational integration of the above, will enable your organization to forestall attrition, improve customer retention or increase up-selling and cross-selling. A structurally and semantically consistent view of master data can produce other benefits, including: Reduced costs related to mergers and acquisitions. Improved product management. Simplified procurement processes. Improved compliance with privacy legislation. Increased employee productivity. Refined fraud prevention. Reduced overall spending. Increased revenue and customer share. Improved operational risk management. Optimized marketing and sales promotions. Reduced supplier onboarding costs. Improved supply chain management. Introducing MDM is just the first undertaking for your organization to gain these benefits; the trick is specifying the concrete methods by which you can achieve them. And the first step is in readying your organization for change. 3
6 SAS White Paper Assessing Organizational Readiness for Enterprise Initiatives While MDM supports all the well-defined business value drivers, installing an MDM tool does not necessarily guarantee results. Prematurely deploying technical platforms in anticipation of the business benefits can only lead to missed expectations, and consequently, the perception of program failure. You can gain true value when key stakeholders understand that the technology is only part of the solution, and the organization needs to realign its processes and retrain its staff to get the best advantage from comprehensive customer and product visibility. Common barriers to achieving success for MDM at the enterprise level are: Horizontal coordination Ensuring effective coordination across numerous lines of business (this is a people issue). Coordinated systemic change Managing the need for changes to business processes along with master data views (this is a process issue). Consistency in meaning Simplifying the processes for resolving semantic inconsistency (this is a knowledge management issue). Standardized business rules Using MDM to manage and enforce business rules, based upon agreed data standards (this is a technology issue). Your organization can more effectively take advantage of enterprise master data when the senior managers are ready, willing and able to make fundamental holistic changes to business as usual. This may range from simple process tweaks to wholesale changes to key business processes, incentive factors and compensation models. In fact, without understanding the need for these change management imperatives, your organization will probably not fully benefit from a unified view of critical data sets provided by enterprise MDM. You can gain true value when key stakeholders understand that the technology is only part of the solution, and the organization needs to realign its processes and retrain its staff to get the best advantage from comprehensive customer and product visibility. Horizontal Coordination Most business processes are developed in the context of the day-to-day operational needs. Applications collect data without the staff members considering how other areas of the organization might repurpose and reuse that information. In contrast to the distinct awareness of each function s own data uses, a degree of myopia takes over once the data leaves the functional silo. People don t recognize issues until they need to analyze errors that have bubbled up to the surface, often when they have led to internal impacts or even spread out to the customers. Requests to fix issues cause alarm, especially when the issue has a business impact such as protection of private data, negative perception of the corporate brand, loss of opportunities or increased costs. The severity of the issues may prompt an increased focus on data issues; this reactivity is a characteristic of limited maturity in taking advantage of data as an enterprise asset. 4
7 Practical Fundamentals for Master Data Management This means that even with master data repositories, everyone s focus will remain on the silo until better practices for cross-functional data coordination are put into place. The areas of the business will benefit from improved data quality, but improved data exploitation evolves as the organization develops better approaches for soliciting, documenting and internalizing data usage and quality requirements from across the different lines of business. Coordinating Systemic Change We can bundle business initiatives using terms like customer-centricity and brand enhancement that demand the use of master data. However, scratch the surface and the actual tasks involved with taking advantage of customer-centricity involve both the data and the business processes. Improved data exploitation evolves as the organization develops better approaches for soliciting, documenting and internalizing data usage and quality requirements from across the different lines of business. For example, saying that complete customer or product data visibility will lead to better up-selling and cross-selling does not tell your customer-facing staff how to use customer-product data visibility to make more (or larger) sales. Instead, establishing sales policies will direct the sales agent to increase cross-sell revenue by offering a multiple-product discount. A change to the business process will employ customerproduct data to help identify opportunities when a customer is eligible for the multipleproduct discount, and it can inform the sales agent on approaches to using that master data to increase sales volume. Changing the data without changing the process may provide you with more accurate, complete and consistent data, but it will not lead you to the promised strategic benefits. You can only achieve the promise of revolutionary business benefits when your organization is prepared to evaluate and potentially reengineer the business processes to put the master data to its best use. Consistency in Meaning Years of siloed functions and the corresponding distributed application development create an environment in which the same business terms are used over and over again without there ever being a common understanding of those terms specific definition. The growing desire for reporting and analysis has driven the integration of data from multiple sources into centralized data warehouses. But the absence of consistent semantics has led to confusion in the reports, with a corresponding decrease in trust in the results followed by an increased effort for reconciling the differences in understanding. Building the unified view of the key data domains as part of a master data management program often exposes these situations. Standardized Business Rules You can only achieve the promise of revolutionary business benefits when your organization is prepared to evaluate and potentially reengineer the business processes to put the master data to its best use. Consistency issues are not limited to business term and data element definitions. If different groups within your organization have similar data needs, they may also share similar sets of business rules. Again, applications developed in virtual vacuums are subject to variation when it comes to defining and implementing business rules. Standardization of business terminology and associated definitions will enable business rule standardization, but until then, your business may be limited in taking advantage of common master data. 5
8 SAS White Paper Practical Master Data Management Before crossing these barriers, your organization is less likely to employ master data management to address the key business value drivers. In fact, the desire to deploy the technology first suggests that the key players might not yet be ready to make those serious changes necessary to address their business challenges. Although your organization might not be ready to take full advantage of MDM, it can still derive value through a better shared understanding of data semantics, defining data standards, standardizing business rules and improving data quality. This is in line with the third type of purported MDM benefits: technical data improvement benefits derived from better data quality and data governance practices. Technical Data Improvement Creating a unified view of master data reduces variability in data and essentially forces the technologists to refine their definitions, document their shared metadata and review how discrepancies in the data influence day-to-day operations. From this perspective, initiating the data quality and data governance aspects of an MDM activity addresses the fundamentals of data sharing and reuse. This accounts for our technical MDM benefits, such as: Promoting consistent use of data. Increasing reporting accuracy. Improving data sharing and usability. Standardizing data validation. Increasing data completeness and consistency. Complementing a services-oriented architecture. Achieving these practical, data-oriented benefits not only helps you align the data with organizational information requirements while providing knowledge and expertise to make MDM projects repeatable and scalable, it also helps prepare your key stakeholders to take the first steps toward organizational change. Although your organization might not be ready to take full advantage of MDM, it can still derive value through a better shared understanding of data semantics, defining data standards, standardizing business rules and improving data quality. Achieving these practical, data-oriented benefits not only helps you align the data with organizational information requirements while providing knowledge and expertise to make MDM projects repeatable and scalable, it also helps prepare your key stakeholders to take the first steps toward organizational change. 6
9 Practical Fundamentals for Master Data Management Reconsidering MDM To start with the fundamentals effectively means to reconsider the drivers, the value proposition and the plan for MDM. While it is reasonable to anticipate the strategic business benefits, it is critical to recognize that the business processes must first mature along with the underlying data infrastructure. A steady progression of value begins with the data silos and, over time, transitions across the areas of the business, as shown in Figure 1. ANALYTICAL OPERATIONAL LINE OF BUSINESS Data improvements TIME Tactical efficiencies ENTERPRISE Strategic value generation Instead of taking the comprehensive boil the ocean enterprise approach to design and implementation, you can take a fundamentals approach that focuses on the critical data-oriented improvements such as metadata management, data standards, data quality management and data governance. Figure 1: Master data management provides incremental value over time. Therefore, instead of taking the comprehensive boil the ocean enterprise approach to design and implementation, you can take a fundamentals approach that focuses on the critical data-oriented improvements such as metadata management, data standards, data quality management and data governance. These aspects are necessary for addressing specific and immediate operational data deficiencies that are also keys to MDM success and will provide concrete benefits, at least through the improvement of the quality of data used within each line of business. Purpose-Driven MDM You cannot overlook the need to identify business targets to be practical beneficiaries for each aspect of the MDM project. Your organization must: 1) determine those business processes that have acute data issues along with measurable business impact; and 2) assess the degree to which a master data task alleviates those immediate issues. Then, you can use each activity to prepare the organization to make the real changes in alignment, communication and business process reengineering. Scoping MDM projects to be business-purpose-driven allows for a modular evolution of the MDM capability. Scoping MDM projects to be business-purpose-driven allows for a modular evolution of the MDM capability. 7
10 SAS White Paper Start with the Fundamentals The early tangible value of your MDM strategy comes from our fundamentals: standardized semantics, consolidated metadata and, most critically, data quality improvement. By focusing on these fundamental aspects of MDM, you will reduce the risks while providing practical utility to the data user community. Standardize Semantics Siloed database and application development harbors loosely defined business terms. To demonstrate this, think about the number of different ways the terms customer or product are used in different areas of your business operations. Aggregating customer data from across the enterprise may provide you with a consolidated database, but the different meanings for the term will lead to inconsistency, reconciliation and a general lack of trust in reported results unless the records are known to refer to the same entity. Standardizing semantics is a process that involves these steps: 1. Identify the business process uses of common business terms. 2. Document a definition of the term within each business context. 3. Determine and document those definitions that are equivalent or consistent (there may be more than one!). 4. Identify and qualify those uses of the business term in which the definition is not consistent. The early tangible value of your MDM strategy comes from our fundamentals: standardized semantics, consolidated metadata and, most critically, data quality improvement. As an example, the sales department may consider the primary purchasing contact at a company to be a customer, while the service department may provide service to any customer at the company that uses the product. These are actually two different underlying definitions and should be qualified as such. In turn, for any pair of data sets, you can aggregate the records representing a concept into a single set as long as the definitions of each concept are equivalent or consistent. Consolidate Enterprise Metadata Documenting business term definitions collected from across the organization is one small part of a more comprehensive approach to capturing enterprise metadata. Consistency in definition goes beyond the term to include data element naming, types, structure, formatting patterns, table structures and cross-table relations, and how all these artifacts are employed in the different business processes and corresponding applications. Establishing line-of-sight from the business term (as it is used within a defined business policy) throughout the organization simplifies the required data integration processes and reduces effort in incrementally building master data repositories. This also enables rapid impact analysis when underlying policies imply adjustments or wholesale changes to data element usage, definitions or structures. 8
11 Practical Fundamentals for Master Data Management Improve Data Quality Because many MDM programs are essentially predicated on the need for more effective sharing of trustworthy information, the aspects of MDM that encompass data quality improvement are likely to deliver business benefits that will extend beyond the funding cycle for an MDM project. These benefits include: Processes for data quality assessment. Evaluation of suitability of data elements as identifying attributes for matching and linkage. Improved triage and prioritization for identified data issues. Proactive validation of data. Balance Business and Technical Benefits Most business processes will benefit from better data management practices in terms of operational efficiency, reduced complexity and more consistent reporting and analysis. The information management teams can use these tactical efficiencies as a bridge to achieving the strategic business goals. Ultimately, though, the MDM program needs to address the key business issues attributed to data utilization as reflected through improved measures of key performance indicators. The surest way to lose momentum is to confine the MDM project to IT-only success factors, such as IT consolidation or efficiency. While the technical benefits are useful for establishing visibility, you would have difficulty sustaining the program without creating business value. In other words, when your organization can achieve bottom-line results such as compliance improvement, improved cross-selling, customer retention, and marketing and procurement cost savings, it can realize the long-term strategic value of sustainable master data management. When your organization can achieve bottom-line results such as compliance improvement, improved crossselling, customer retention, and marketing and procurement cost savings, it can realize the long-term strategic value of sustainable master data management. 9
12 SAS White Paper Establishing the Fundamentals Producing early success is a key strategy for realizing the full benefits of MDM. It is therefore incumbent upon the data strategist to determine the path of least resistance for introducing the fundamental concepts and achieving those early successes while planning how the data strategy will align with the necessary changes to the business. Here are three concrete steps you should take on this path: 1. Readiness evaluation Evaluate the degree to which the key stakeholders are prepared to make the necessary changes to best take advantage of master data. 2. Data quality assessment Determine whether there are well-developed processes for assessing data quality within the different business contexts. 3. Establish business imperatives Enumerate and prioritize immediate lowhanging fruit data standards, metadata and data quality improvement tasks. Organizational preparedness means that your key stakeholders are willing to identify flawed processes and make the serious changes needed to help those processes work the right way. Ready? Organizational preparedness means that your key stakeholders are willing to identify flawed processes and make the serious changes needed to help those processes work the right way. Gauging this preparedness involves serious organizational introspection. For example, your organization should ask (and satisfactorily answer) the following questions: Are we willing to map the end-to-end processes and identify dependencies on a unified view of the data? Does each of our business processes have well-defined success metrics? Do we have measures for the success metrics, and are they actually measured? Are we willing to evaluate and eliminate inefficiencies resulting from duplicated information? Do we have established processes for assessing the business impact of poor data management practices? Have we engineered incentive and compensation frameworks to reward creation and maintenance of high-quality data? A repeatable process for soliciting business impacts and framing them in terms of data quality issues enables a quantifiable assessment of data quality that can be used to calculate ROI for data improvement activities. Set We have already stated that many MDM programs are predicated on the need for more effective sharing of trustworthy information. But to gain the benefits of improved data quality, you must know which data sets are flawed, why they are flawed, and the scope and scale of the business impacts of those flaws. A repeatable process for soliciting business impacts and framing them in terms of data quality issues enables a quantifiable assessment of data quality that can be used to calculate ROI for data improvement activities. 10
13 Practical Fundamentals for Master Data Management Go! Lastly, you can offset these calculated returns on investment against the anticipated costs of designing and implementing each of the identified data improvement activities. The difference between the potential return and the costs to improve provides you with an absolute measure of the financial value of each of the improvement opportunities. You can prioritize these tasks in terms of at least these three dimensions: 1. The financial value, which is the absolute financial potential. 2. The time to value, which is the time between the start of the task and the time that the benefit is seen. 3. The strategic value, which is defined in terms of the degree to which the activity contributes to the long-term data strategy. You can use these dimensions to prioritize the low-hanging fruit that provides quantifiable value while incrementally contributing to meeting strategic objectives. By simplifying and standardizing semantics, focusing on metadata management, and improving data quality, your information management teams can address fundamental needs that will best add value while preparing your organization to build an effective master data capability that is the cornerstone of your enterprise information management program. You can offset these calculated returns on investment against the anticipated costs of designing and implementing each of the identified data improvement activities. Learn more To learn more about MDM, visit: dataflux.com/knowledgecenter/mdm About the Author David Loshin, President of Knowledge Integrity Inc., is a recognized thought leader and expert consultant in the areas of data quality, master data management and business intelligence. Loshin is a prolific author regarding data management best practices and has written numerous books, white papers and Web seminars on a variety of data management best practices. His book, Business Intelligence: The Savvy Manager s Guide has been hailed as a resource allowing readers to gain an understanding of business intelligence, business management disciplines, data warehousing and how all of the pieces work together. His book, Master Data Management, has been endorsed by data management industry leaders, and his valuable MDM insights can be reviewed at mdmbook.com. Loshin is also the author of the recent book The Practitioner s Guide to Data Quality Improvement. He can be reached at loshin@knowledge-integrity.com. 11
14 About SAS SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 60,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world THE POWER TO KNOW. For more information on SAS Business Analytics software and services, visit sas.com. SAS Institute Inc. World Headquarters To contact your local SAS office, please visit: sas.com/offices SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. indicates USA registration. Other brand and product names are trademarks of their respective companies. Copyright 2012, SAS Institute Inc. All rights reserved _S97433_0912
Master Data Management Drivers: Fantasy, Reality and Quality
Solutions for Customer Intelligence, Communications and Care. Master Data Management Drivers: Fantasy, Reality and Quality A Review and Classification of Potential Benefits of Implementing Master Data
More informationThree Fundamental Techniques To Maximize the Value of Your Enterprise Data
Three Fundamental Techniques To Maximize the Value of Your Enterprise Data Prepared for Talend by: David Loshin Knowledge Integrity, Inc. October, 2010 2010 Knowledge Integrity, Inc. 1 Introduction Organizations
More informationBusting 7 Myths about Master Data Management
Knowledge Integrity Incorporated Busting 7 Myths about Master Data Management Prepared by: David Loshin Knowledge Integrity, Inc. August, 2011 Sponsored by: 2011 Knowledge Integrity, Inc. 1 (301) 754-6350
More information5 Best Practices for SAP Master Data Governance
5 Best Practices for SAP Master Data Governance By David Loshin President, Knowledge Integrity, Inc. Sponsored by Winshuttle, LLC 2012 Winshuttle, LLC. All rights reserved. 4/12 www.winshuttle.com Introduction
More informationBuilding a Data Quality Scorecard for Operational Data Governance
Building a Data Quality Scorecard for Operational Data Governance A White Paper by David Loshin WHITE PAPER Table of Contents Introduction.... 1 Establishing Business Objectives.... 1 Business Drivers...
More informationChallenges in the Effective Use of Master Data Management Techniques WHITE PAPER
Challenges in the Effective Use of Master Management Techniques WHITE PAPER SAS White Paper Table of Contents Introduction.... 1 Consolidation: The Typical Approach to Master Management. 2 Why Consolidation
More informationData Governance, Data Architecture, and Metadata Essentials
WHITE PAPER Data Governance, Data Architecture, and Metadata Essentials www.sybase.com TABLE OF CONTENTS 1 The Absence of Data Governance Threatens Business Success 1 Data Repurposing and Data Integration
More informationSupporting Your Data Management Strategy with a Phased Approach to Master Data Management WHITE PAPER
Supporting Your Data Strategy with a Phased Approach to Master Data WHITE PAPER SAS White Paper Table of Contents Changing the Way We Think About Master Data.... 1 Master Data Consumers, the Information
More informationUnderstanding the Financial Value of Data Quality Improvement
Understanding the Financial Value of Data Quality Improvement Prepared by: David Loshin Knowledge Integrity, Inc. January, 2011 Sponsored by: 2011 Knowledge Integrity, Inc. 1 Introduction Despite the many
More informationOperationalizing Data Governance through Data Policy Management
Operationalizing Data Governance through Data Policy Management Prepared for alido by: David Loshin nowledge Integrity, Inc. June, 2010 2010 nowledge Integrity, Inc. Page 1 Introduction The increasing
More informationPopulating a Data Quality Scorecard with Relevant Metrics WHITE PAPER
Populating a Data Quality Scorecard with Relevant Metrics WHITE PAPER SAS White Paper Table of Contents Introduction.... 1 Useful vs. So-What Metrics... 2 The So-What Metric.... 2 Defining Relevant Metrics...
More informationConsiderations: Mastering Data Modeling for Master Data Domains
Considerations: Mastering Data Modeling for Master Data Domains David Loshin President of Knowledge Integrity, Inc. June 2010 Americas Headquarters EMEA Headquarters Asia-Pacific Headquarters 100 California
More informationData Governance for Master Data Management and Beyond
Data Governance for Master Data Management and Beyond A White Paper by David Loshin WHITE PAPER Table of Contents Aligning Information Objectives with the Business Strategy.... 1 Clarifying the Information
More informationHarness the value of information throughout the enterprise. IBM InfoSphere Master Data Management Server. Overview
IBM InfoSphere Master Data Management Server Overview Master data management (MDM) allows organizations to generate business value from their most important information. Managing master data, or key business
More informationIBM Software A Journey to Adaptive MDM
IBM Software A Journey to Adaptive MDM What is Master Data? Why is it Important? A Journey to Adaptive MDM Contents 2 MDM Business Drivers and Business Value 4 MDM is a Journey 7 IBM MDM Portfolio An Adaptive
More informationEffecting Data Quality Improvement through Data Virtualization
Effecting Data Quality Improvement through Data Virtualization Prepared for Composite Software by: David Loshin Knowledge Integrity, Inc. June, 2010 2010 Knowledge Integrity, Inc. Page 1 Introduction The
More information5 Best Practices for SAP Master Data Governance
5 Best Practices for SAP Master Data Governance By David Loshin President, Knowledge Integrity, Inc. Sponsored by Winshuttle, LLC Executive Summary Successful deployment of ERP solutions can revolutionize
More informationData Governance, Data Architecture, and Metadata Essentials Enabling Data Reuse Across the Enterprise
Data Governance Data Governance, Data Architecture, and Metadata Essentials Enabling Data Reuse Across the Enterprise 2 Table of Contents 4 Why Business Success Requires Data Governance Data Repurposing
More informationEnterprise Data Governance
DATA GOVERNANCE Enterprise Data Governance Strategies and Approaches for Implementing a Multi-Domain Data Governance Model Mark Allen Sr. Consultant, Enterprise Data Governance WellPoint, Inc. 1 Introduction:
More informationWhat to Look for When Selecting a Master Data Management Solution
What to Look for When Selecting a Master Data Management Solution What to Look for When Selecting a Master Data Management Solution Table of Contents Business Drivers of MDM... 3 Next-Generation MDM...
More informationIntegrating Data Governance into Your Operational Processes
TDWI rese a rch TDWI Checklist Report Integrating Data Governance into Your Operational Processes By David Loshin Sponsored by tdwi.org August 2011 TDWI Checklist Report Integrating Data Governance into
More informationOPTIMUS SBR. Optimizing Results with Business Intelligence Governance CHOICE TOOLS. PRECISION AIM. BOLD ATTITUDE.
OPTIMUS SBR CHOICE TOOLS. PRECISION AIM. BOLD ATTITUDE. Optimizing Results with Business Intelligence Governance This paper investigates the importance of establishing a robust Business Intelligence (BI)
More informationIT Financial Management and Cost Recovery
WHITE PAPER November 2010 IT Financial Management and Cost Recovery Patricia Genetin Sr. Principal Consultant/CA Technical Sales David Messineo Sr. Services Architect/CA Services Table of Contents Executive
More informationBusiness Intelligence
Transforming Information into Business Intelligence Solutions Business Intelligence Client Challenges The ability to make fast, reliable decisions based on accurate and usable information is essential
More informationData Governance. Unlocking Value and Controlling Risk. Data Governance. www.mindyourprivacy.com
Data Governance Unlocking Value and Controlling Risk 1 White Paper Data Governance Table of contents Introduction... 3 Data Governance Program Goals in light of Privacy... 4 Data Governance Program Pillars...
More informationThere s more to Master Data Management than Mastering Data. Andrew Bonanni, Dataport Solutions abonanni@dataportsolutions.com
There s more to Master Data Management than Mastering Data Andrew Bonanni, Dataport Solutions abonanni@dataportsolutions.com Organizations that follow a disciplined approach to cross-functional workflow
More informationAgile Master Data Management A Better Approach than Trial and Error
Agile Master Data Management A Better Approach than Trial and Error A whitepaper by First San Francisco Partners First San Francisco Partners Whitepaper Executive Summary Market leading corporations are
More informationCustomer Experience Strategy and Implementation
Customer Experience Strategy and Implementation Enterprise Customer Experience Transformation 2014 Andrew Reise, LLC. All Rights Reserved. Enterprise Customer Experience Transformation Executive Summary
More informationdxhub Denologix MDM Solution Page 1
Most successful large organizations are organized by lines of business (LOB). This has been a very successful way to organize for the accountability of profit and loss. It gives LOB leaders autonomy to
More informationFortune 500 Medical Devices Company Addresses Unique Device Identification
Fortune 500 Medical Devices Company Addresses Unique Device Identification New FDA regulation was driver for new data governance and technology strategies that could be leveraged for enterprise-wide benefit
More informationData Governance. Data Governance, Data Architecture, and Metadata Essentials Enabling Data Reuse Across the Enterprise
Data Governance Data Governance, Data Architecture, and Metadata Essentials Enabling Data Reuse Across the Enterprise 2 Table of Contents 4 Why Business Success Requires Data Governance Data Repurposing
More informationEnable Business Agility and Speed Empower your business with proven multidomain master data management (MDM)
Enable Business Agility and Speed Empower your business with proven multidomain master data management (MDM) Customer Viewpoint By leveraging a well-thoughtout MDM strategy, we have been able to strengthen
More informationWhitepaper Data Governance Roadmap for IT Executives Valeh Nazemoff
Whitepaper Data Governance Roadmap for IT Executives Valeh Nazemoff The Challenge IT Executives are challenged with issues around data, compliancy, regulation and making confident decisions on their business
More informationSUSTAINING COMPETITIVE DIFFERENTIATION
SUSTAINING COMPETITIVE DIFFERENTIATION Maintaining a competitive edge in customer experience requires proactive vigilance and the ability to take quick, effective, and unified action E M C P e r s pec
More informationFive Fundamental Data Quality Practices
Five Fundamental Data Quality Practices W H I T E PA P E R : DATA QUALITY & DATA INTEGRATION David Loshin WHITE PAPER: DATA QUALITY & DATA INTEGRATION Five Fundamental Data Quality Practices 2 INTRODUCTION
More informationCloud Computing. Key Initiative Overview
David W. Cearley Research Vice President and Gartner Fellow This overview provides a high-level description of the Cloud Computing Key Initiative. IT leaders can use this guide to understand what they
More informationData Quality and Cost Reduction
Data Quality and Cost Reduction A White Paper by David Loshin WHITE PAPER SAS White Paper Table of Contents Introduction Data Quality as a Cost-Reduction Technique... 1 Understanding Expenses.... 1 Establishing
More informationBUSINESS INTELLIGENCE MATURITY AND THE QUEST FOR BETTER PERFORMANCE
WHITE PAPER BUSINESS INTELLIGENCE MATURITY AND THE QUEST FOR BETTER PERFORMANCE Why most organizations aren t realizing the full potential of BI and what successful organizations do differently Research
More informationTop 10 Trends In Business Intelligence for 2007
W H I T E P A P E R Top 10 Trends In Business Intelligence for 2007 HP s New Information Management Practice Table of contents Trend #1: BI Governance: Ensuring the Effectiveness of Programs and Investments
More informationData Integration Alternatives Managing Value and Quality
Solutions for Customer Intelligence, Communications and Care. Data Integration Alternatives Managing Value and Quality Using a Governed Approach to Incorporating Data Quality Services Within the Data Integration
More informationData Governance: A Business Value-Driven Approach
Data Governance: A Business Value-Driven Approach A White Paper by Dr. Walid el Abed CEO January 2011 Copyright Global Data Excellence 2011 Contents Executive Summary......................................................3
More informationIBM Information Management
IBM Information Management January 2008 IBM Information Management software Enterprise Information Management, Enterprise Content Management, Master Data Management How Do They Fit Together An IBM Whitepaper
More informationCustomer Experience Management (CEM) Technology: What, Why, and How Does It Work
Customer Experience Management (CEM) Technology: What, Why, and How Does It Work February 2005 Executive Summary A new class of technology has arrived. Customer Experience Management (CEM) Technology delivers
More informationInformation Management & Data Governance
Data governance is a means to define the policies, standards, and data management services to be employed by the organization. Information Management & Data Governance OVERVIEW A thorough Data Governance
More informationData Governance Baseline Deployment
Service Offering Data Governance Baseline Deployment Overview Benefits Increase the value of data by enabling top business imperatives. Reduce IT costs of maintaining data. Transform Informatica Platform
More informationNCOE whitepaper Master Data Deployment and Management in a Global ERP Implementation
NCOE whitepaper Master Data Deployment and Management in a Global ERP Implementation Market Offering: Package(s): Oracle Authors: Rick Olson, Luke Tay Date: January 13, 2012 Contents Executive summary
More informationBANKING ON CUSTOMER BEHAVIOR
BANKING ON CUSTOMER BEHAVIOR How customer data analytics are helping banks grow revenue, improve products, and reduce risk In the face of changing economies and regulatory pressures, retail banks are looking
More informationData Governance: A Business Value-Driven Approach
Global Excellence Governance: A Business Value-Driven Approach A White Paper by Dr Walid el Abed CEO Trusted Intelligence Contents Executive Summary......................................................3
More informationCONNECTING DATA WITH BUSINESS
CONNECTING DATA WITH BUSINESS Big Data and Data Science consulting Business Value through Data Knowledge Synergic Partners is a specialized Big Data, Data Science and Data Engineering consultancy firm
More informationHow to Manage Your Data as a Strategic Information Asset
How to Manage Your Data as a Strategic Information Asset CONCLUSIONS PAPER Insights from a webinar in the 2012 Applying Business Analytics Webinar Series Featuring: Mark Troester, Former IT/CIO Thought
More informationConnecting data initiatives with business drivers
Connecting data initiatives with business drivers TABLE OF CONTENTS: Introduction...1 Understanding business drivers...2 Information requirements and data dependencies...3 Costs, benefits, and low-hanging
More informationHow to Create a Culture of Customer Centricity
Solutions for Enabling Lifetime Customer Relationships Forging a Culture of Customer Centricity Using an Alternative Approach to Master Data Management W HITE PAPER: MASTER DATA MANAGEMENT WHITE PAPER:
More informationWhy is Master Data Management getting both Business and IT Attention in Today s Challenging Economic Environment?
Why is Master Data Management getting both Business and IT Attention in Today s Challenging Economic Environment? How Can You Gear-up For Your MDM initiative? Tamer Chavusholu, Enterprise Solutions Practice
More informationStrategic Planning. Key Initiative Overview
David Aron Research Vice President This overview provides a high-level description of the Strategic Planning Key Initiative. IT leaders can use it to create strategies that help the business win, and change
More informationEnterprise Data Quality
Enterprise Data Quality An Approach to Improve the Trust Factor of Operational Data Sivaprakasam S.R. Given the poor quality of data, Communication Service Providers (CSPs) face challenges of order fallout,
More informationMDM Components and the Maturity Model
A DataFlux White Paper Prepared by: David Loshin MDM Components and the Maturity Model Leader in Data Quality and Data Integration www.dataflux.com 877 846 FLUX International +44 (0) 1753 272 020 One common
More informationBuild an effective data integration strategy to drive innovation
IBM Software Thought Leadership White Paper September 2010 Build an effective data integration strategy to drive innovation Five questions business leaders must ask 2 Build an effective data integration
More informationEnabling Data Quality
Enabling Data Quality Establishing Master Data Management (MDM) using Business Architecture supported by Information Architecture & Application Architecture (SOA) to enable Data Quality. 1 Background &
More informationData Integration Alternatives Managing Value and Quality
Solutions for Enabling Lifetime Customer Relationships Data Integration Alternatives Managing Value and Quality Using a Governed Approach to Incorporating Data Quality Services Within the Data Integration
More informationTDWI strives to provide course books that are content-rich and that serve as useful reference documents after a class has ended.
Previews of TDWI course books offer an opportunity to see the quality of our material and help you to select the courses that best fit your needs. The previews cannot be printed. TDWI strives to provide
More informationManagement Update: The Eight Building Blocks of CRM
IGG-06252003-01 S. Nelson Article 25 June 2003 Management Update: The Eight Building Blocks of CRM Customer relationship management (CRM) represents the key business strategy that will determine successful
More informationBest Practices in Enterprise Data Governance
Best Practices in Enterprise Data Governance Scott Gidley and Nancy Rausch, SAS WHITE PAPER SAS White Paper Table of Contents Introduction.... 1 Data Governance Use Case and Challenges.... 1 Collaboration
More informationBusiness Performance & Data Quality Metrics. David Loshin Knowledge Integrity, Inc. loshin@knowledge-integrity.com (301) 754-6350
Business Performance & Data Quality Metrics David Loshin Knowledge Integrity, Inc. loshin@knowledge-integrity.com (301) 754-6350 1 Does Data Integrity Imply Business Value? Assumption: improved data quality,
More informationHow To Understand And Understand The Concept Of Business Architecture
WHITE PAPER Business Architecture: Dispelling Ten Common Myths William Ulrich, TSG, Inc. Whynde Kuehn, S2E Consulting Inc. Business Architecture: An Evolving Discipline B usiness architecture is a maturing
More informationA Hyperion System Overview. Hyperion System 9
A Hyperion System Overview Hyperion System 9 Your organization relies on multiple transactional systems including ERP, CRM, and general ledger systems to run your business. In today s business climate
More informationThe Infrastructure for Information Management: A Brave New World for the CIO WHITE PAPER
The Infrastructure for Information Management: A Brave New World for the CIO WHITE PAPER SAS White Paper Table of Contents Trends and Drivers for Information Infrastructure.... 1 Objectives for Organizational
More informationEnhancing Sales and Operations Planning with Forecasting Analytics and Business Intelligence WHITE PAPER
Enhancing Sales and Operations Planning with Forecasting Analytics and Business Intelligence WHITE PAPER SAS White Paper Table of Contents Introduction.... 1 Analytics.... 1 Forecast Cycle Efficiencies...
More informationSuccessful Enterprise Architecture. Aligning Business and IT
Successful Enterprise Architecture Aligning Business and IT 1 Business process SOLUTIONS WHITE PAPER Executive Summary...3 An Integrated Business & IT Infrastructure...3 Benefits to Business and IT Go
More informationThe SAS Transformation Project Deploying SAS Customer Intelligence for a Single View of the Customer
Paper 3353-2015 The SAS Transformation Project Deploying SAS Customer Intelligence for a Single View of the Customer ABSTRACT Pallavi Tyagi, Jack Miller and Navneet Tuteja, Slalom Consulting. Building
More informationPoint of View: FINANCIAL SERVICES DELIVERING BUSINESS VALUE THROUGH ENTERPRISE DATA MANAGEMENT
Point of View: FINANCIAL SERVICES DELIVERING BUSINESS VALUE THROUGH ENTERPRISE DATA MANAGEMENT THROUGH ENTERPRISE DATA MANAGEMENT IN THIS POINT OF VIEW: PAGE INTRODUCTION: A NEW PATH TO DATA ACCURACY AND
More information!!!!! White Paper. Understanding The Role of Data Governance To Support A Self-Service Environment. Sponsored by
White Paper Understanding The Role of Data Governance To Support A Self-Service Environment Sponsored by Sponsored by MicroStrategy Incorporated Founded in 1989, MicroStrategy (Nasdaq: MSTR) is a leading
More informationApplied Business Intelligence. Iakovos Motakis, Ph.D. Director, DW & Decision Support Systems Intrasoft SA
Applied Business Intelligence Iakovos Motakis, Ph.D. Director, DW & Decision Support Systems Intrasoft SA Agenda Business Drivers and Perspectives Technology & Analytical Applications Trends Challenges
More informationThe case for Centralized Customer Decisioning
IBM Software Thought Leadership White Paper July 2011 The case for Centralized Customer Decisioning A white paper written by James Taylor, Decision Management Solutions. This paper was produced in part
More informationService Oriented Architecture and the DBA Kathy Komer Aetna Inc. New England DB2 Users Group. Tuesday June 12 1:00-2:15
Service Oriented Architecture and the DBA Kathy Komer Aetna Inc. New England DB2 Users Group Tuesday June 12 1:00-2:15 Service Oriented Architecture and the DBA What is Service Oriented Architecture (SOA)
More informationCMDB Essential to Service Management Strategy. All rights reserved 2007
CMDB: Essential to the Service Management strategy Business Proposition: This white paper describes how the CMDB is an essential component of the IT Service Management Strategy, and why the FrontRange
More informationCOM-19-1029 A. White, D. Hope-Ross, K. Peterson, D. Ackerman
A. White, D. Hope-Ross, K. Peterson, D. Ackerman Research Note 7 February 2003 Commentary Product Content and Data Management Promises Savings By 2013, standardized ways of describing products will prevent
More informationGOING BEYOND TRADITIONAL DATA GOVERNANCE: SIX STEPS FOR BUILDING AN INFLUENTIAL DATA-DRIVEN ORGANIZATION
GOING BEYOND TRADITIONAL DATA GOVERNANCE: SIX STEPS FOR BUILDING AN INFLUENTIAL DATA-DRIVEN ORGANIZATION In an environment of rapidly evolving technology, customer behavior, and competition, data has become
More informationProcess-Centric Back Office Transformation
Industry Insights Banking Process-Centric Back Office Transformation Executive Summary By driving back-office efficiency, banks and other financial institutions seek to lower expenses and reduce business
More informationIncrease Business Intelligence Infrastructure Responsiveness and Reliability Using IT Automation
White Paper Increase Business Intelligence Infrastructure Responsiveness and Reliability Using IT Automation What You Will Learn That business intelligence (BI) is at a critical crossroads and attentive
More informationToday s shared services operating models: The engine behind enterprise transformation
IBM Global Process Services Thought Leadership White Paper December 2011 Today s shared services operating models: The engine behind enterprise transformation Leveraging the power of globally integrated
More informationMaster Your Data. Master Your Business. Empower your business with access to consolidated and reliable business-critical data
Master Your. Master Your Business. Empower your business with access to consolidated and reliable business-critical data Award-winning Informatica MDM provides reliable views of business-critical data
More informationAn Enterprise Framework for Business Intelligence
An Enterprise Framework for Business Intelligence Colin White BI Research May 2009 Sponsored by Oracle Corporation TABLE OF CONTENTS AN ENTERPRISE FRAMEWORK FOR BUSINESS INTELLIGENCE 1 THE BI PROCESSING
More informationThe Power of Personalizing the Customer Experience
The Power of Personalizing the Customer Experience Creating a Relevant Customer Experience from Real-Time, Cross-Channel Interaction WHITE PAPER SAS White Paper Table of Contents The Marketplace Today....1
More informationMaster data deployment and management in a global ERP implementation
Master data deployment and management in a global ERP implementation Contents Master data management overview Master data maturity and ERP Master data governance Information management (IM) Business processes
More informationRealizing business flexibility through integrated SOA policy management.
SOA policy management White paper April 2009 Realizing business flexibility through integrated How integrated management supports business flexibility, consistency and accountability John Falkl, distinguished
More informationImplementing a Data Governance Initiative
Implementing a Data Governance Initiative Presented by: Linda A. Montemayor, Technical Director AT&T Agenda AT&T Business Alliance Data Governance Framework Data Governance Solutions: o Metadata Management
More informationKnowing the customer: this time it s personal. How analytics can help banks achieve superior CRM, secure growth and drive high performance
Knowing the customer: this time it s personal How analytics can help banks achieve superior CRM, secure growth and drive high performance Table of Contents Introduction How advanced analytics changes customer
More informationWhite Paper February 2009. IBM Cognos Supply Chain Analytics
White Paper February 2009 IBM Cognos Supply Chain Analytics 2 Contents 5 Business problems Perform cross-functional analysis of key supply chain processes 5 Business drivers Supplier Relationship Management
More informationAnatomy of an Enterprise Software Delivery Project
Chapter 2 Anatomy of an Enterprise Software Delivery Project Chapter Summary I present an example of a typical enterprise software delivery project. I examine its key characteristics and analyze specific
More informationView Point. Customer Centric banking: A 360 degree view. Abstract. - Ashok Gopinath, Navdeep Gill
View Point Customer Centric banking: A 360 degree view - Ashok Gopinath, Navdeep Gill Abstract Banks today are moving back to basics, shifting attention from complex product offerings to developing greater
More informationInformation Governance Workshop. David Zanotta, Ph.D. Vice President, Global Data Management & Governance - PMO
Information Governance Workshop David Zanotta, Ph.D. Vice President, Global Data Management & Governance - PMO Recognition of Information Governance in Industry Research firms have begun to recognize the
More informationRiversand Technologies, Inc. Powering Accurate Product Information PIM VS MDM VS PLM. A Riversand Technologies Whitepaper
Riversand Technologies, Inc. Powering Accurate Product Information PIM VS MDM VS PLM A Riversand Technologies Whitepaper Table of Contents 1. PIM VS PLM... 3 2. Key Attributes of a PIM System... 5 3. General
More informationMANAGING USER DATA IN A DIGITAL WORLD
MANAGING USER DATA IN A DIGITAL WORLD AIRLINE INDUSTRY CHALLENGES AND SOLUTIONS WHITE PAPER OVERVIEW AND DRIVERS In today's digital economy, enterprises are exploring ways to differentiate themselves from
More informationOperational Risk Management - The Next Frontier The Risk Management Association (RMA)
Operational Risk Management - The Next Frontier The Risk Management Association (RMA) Operational risk is not new. In fact, it is the first risk that banks must manage, even before they make their first
More informationBusiness Architecture Scenarios
The OMG, Business Architecture Special Interest Group Business Architecture Scenarios Principal Authors William Ulrich, President, TSG, Inc. Co chair, OMG BASIG wmmulrich@baymoon.com Neal McWhorter, Principal,
More informationGlobal Headquarters: 5 Speen Street Framingham, MA 01701 USA P.508.872.8200 F.508.935.4015 www.idc.com
Global Headquarters: 5 Speen Street Framingham, MA 01701 USA P.508.872.8200 F.508.935.4015 www.idc.com E X C E R P T I D C M a r k e t S c a p e : U. S. B u s i n e s s C o n s u l t i n g S e r v i c
More informationRealizing the Business Value of Master Data Management (MDM)
perspective Realizing the Business Value of Master Data Management (MDM) - Shashank Gadgil, Vineet Kulkarni Abstract Research shows that 40% of the anticipated value of all business initiatives is never
More informationCA HalvesThe Cost Of Testing IT Controls For Sarbanes-Oxley Compliance With Unified Processes.
TECHNOLOGY BRIEF: REDUCING COST AND COMPLEXITY WITH GLOBAL GOVERNANCE CONTROLS CA HalvesThe Cost Of Testing IT Controls For Sarbanes-Oxley Compliance With Unified Processes. Table of Contents Executive
More informationVeramark White Paper: Reducing Telecom Costs Why Invoice Management is the Best Place to Start. WhitePaper. We innovate. You benefit.
Veramark White Paper: Reducing Telecom Costs Why Invoice Management is the Best Place to Start WhitePaper We innovate. You benefit. Veramark White Paper: Reducing Telecom Costs Why Invoice Management is
More informationVermont Enterprise Architecture Framework (VEAF) Master Data Management (MDM) Abridged Strategy Level 0
Vermont Enterprise Architecture Framework (VEAF) Master Data Management (MDM) Abridged Strategy Level 0 EA APPROVALS EA Approving Authority: Revision
More information